df <- read.csv("merged-new-version2.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]
df
df <- read.csv("merged-variety.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]
df
df <- read.csv("merged-added-functions.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]
df
df$ln_novelty <- log(df$novelty+1)
df$ln_total <- log(df$total+1)
df$ln_exploration <- log(df$exploration+1)
df$group = factor(df$group)
df$ln_len_unique <- log(df$len_unique+1)
df$ln_added_sum <- log(df$added_sum+1)
df
df_new <- df[, sapply(df, is.numeric)]
cor(df_new, use = "complete.obs", method = "spearman" )
X Unnamed..0 phase novelty abs_perform_diff_best Q7_Q7_1 Q7_Q7_2 Q8_Q8_1 Q10
X 1.000000000 1.000000000 0.24234098 -0.04741331 -0.038818179 -0.007028783 -0.05468920 -0.04967287 0.07080519
Unnamed..0 1.000000000 1.000000000 0.24234098 -0.04741331 -0.038818179 -0.007028783 -0.05468920 -0.04967287 0.07080519
phase 0.242340977 0.242340977 1.00000000 0.11614783 -0.087823892 0.000000000 0.00000000 0.00000000 0.00000000
novelty -0.047413312 -0.047413312 0.11614783 1.00000000 -0.269279723 0.080018022 0.18380978 0.15335427 0.08843367
abs_perform_diff_best -0.038818179 -0.038818179 -0.08782389 -0.26927972 1.000000000 0.051617014 -0.15978122 -0.12990581 -0.23656776
Q7_Q7_1 -0.007028783 -0.007028783 0.00000000 0.08001802 0.051617014 1.000000000 0.59849196 0.23554938 0.18107041
Q7_Q7_2 -0.054689204 -0.054689204 0.00000000 0.18380978 -0.159781215 0.598491958 1.00000000 0.30805235 0.25765206
Q8_Q8_1 -0.049672874 -0.049672874 0.00000000 0.15335427 -0.129905810 0.235549382 0.30805235 1.00000000 0.30652732
Q10 0.070805188 0.070805188 0.00000000 0.08843367 -0.236567760 0.181070414 0.25765206 0.30652732 1.00000000
count -0.048992588 -0.048992588 -0.11856704 0.31277705 -0.390547953 -0.021088625 0.03851054 0.04791366 0.12437608
total -0.085035463 -0.085035463 0.21237656 0.35093689 -0.731639428 -0.071894159 0.14138398 0.14739393 0.22020677
user.requirement -0.090544990 -0.090544990 0.17631252 0.25602042 -0.600678779 -0.100219792 0.07490003 0.12716391 0.17775575
infovis -0.065173870 -0.065173870 0.20842670 0.24858512 -0.626350528 -0.027634236 0.15188458 0.12070087 0.18071434
novelty_score 0.021684030 0.021684030 0.16844298 0.25940071 -0.612495128 -0.092662465 0.11024003 0.12790068 0.17506615
exploration -0.137918112 -0.137918112 -0.23051101 0.35969601 -0.113695104 -0.003202928 0.03283455 -0.02093143 0.02079226
Group -0.968135174 -0.968135174 0.00000000 0.13955343 0.002413445 0.012148461 0.06764756 0.06088616 -0.06719948
len_unique -0.012268317 -0.012268317 0.19026679 0.61941904 -0.476219151 0.125547394 0.21213738 0.25660123 0.24104473
added_sum -0.096339112 -0.096339112 -0.12989002 0.42763970 -0.237174055 0.041498199 0.07157854 0.08453075 0.11804975
ln_novelty -0.047413312 -0.047413312 0.11614783 1.00000000 -0.269279723 0.080018022 0.18380978 0.15335427 0.08843367
ln_total -0.085035463 -0.085035463 0.21237656 0.35093689 -0.731639428 -0.071894159 0.14138398 0.14739393 0.22020677
ln_exploration -0.137918112 -0.137918112 -0.23051101 0.35969601 -0.113695104 -0.003202928 0.03283455 -0.02093143 0.02079226
ln_len_unique -0.012268317 -0.012268317 0.19026679 0.61941904 -0.476219151 0.125547394 0.21213738 0.25660123 0.24104473
ln_added_sum -0.096339112 -0.096339112 -0.12989002 0.42763970 -0.237174055 0.041498199 0.07157854 0.08453075 0.11804975
count total user.requirement infovis novelty_score exploration Group len_unique added_sum
X -0.04899259 -0.08503546 -0.09054499 -0.06517387 0.02168403 -0.137918112 -0.968135174 -0.01226832 -0.09633911
Unnamed..0 -0.04899259 -0.08503546 -0.09054499 -0.06517387 0.02168403 -0.137918112 -0.968135174 -0.01226832 -0.09633911
phase -0.11856704 0.21237656 0.17631252 0.20842670 0.16844298 -0.230511006 0.000000000 0.19026679 -0.12989002
novelty 0.31277705 0.35093689 0.25602042 0.24858512 0.25940071 0.359696008 0.139553432 0.61941904 0.42763970
abs_perform_diff_best -0.39054795 -0.73163943 -0.60067878 -0.62635053 -0.61249513 -0.113695104 0.002413445 -0.47621915 -0.23717405
Q7_Q7_1 -0.02108862 -0.07189416 -0.10021979 -0.02763424 -0.09266247 -0.003202928 0.012148461 0.12554739 0.04149820
Q7_Q7_2 0.03851054 0.14138398 0.07490003 0.15188458 0.11024003 0.032834551 0.067647564 0.21213738 0.07157854
Q8_Q8_1 0.04791366 0.14739393 0.12716391 0.12070087 0.12790068 -0.020931428 0.060886156 0.25660123 0.08453075
Q10 0.12437608 0.22020677 0.17775575 0.18071434 0.17506615 0.020792263 -0.067199483 0.24104473 0.11804975
count 1.00000000 0.46015412 0.33312380 0.37702248 0.38263169 0.553706760 0.041699147 0.38531970 0.58243780
total 0.46015412 1.00000000 0.83475255 0.83755499 0.84153034 0.286473499 0.160285056 0.57545048 0.40088417
user.requirement 0.33312380 0.83475255 1.00000000 0.79279588 0.55250554 0.192288215 0.151617873 0.38163565 0.27283404
infovis 0.37702248 0.83755499 0.79279588 1.00000000 0.57296640 0.196935460 0.132983326 0.43612011 0.27590912
novelty_score 0.38263169 0.84153034 0.55250554 0.57296640 1.00000000 0.237021121 0.034241557 0.50959864 0.36169269
exploration 0.55370676 0.28647350 0.19228822 0.19693546 0.23702112 1.000000000 0.109072879 0.33885513 0.89826000
Group 0.04169915 0.16028506 0.15161787 0.13298333 0.03424156 0.109072879 1.000000000 0.09829201 0.09539310
len_unique 0.38531970 0.57545048 0.38163565 0.43612011 0.50959864 0.338855129 0.098292010 1.00000000 0.54850475
added_sum 0.58243780 0.40088417 0.27283404 0.27590912 0.36169269 0.898259998 0.095393103 0.54850475 1.00000000
ln_novelty 0.31277705 0.35093689 0.25602042 0.24858512 0.25940071 0.359696008 0.139553432 0.61941904 0.42763970
ln_total 0.46015412 1.00000000 0.83475255 0.83755499 0.84153034 0.286473499 0.160285056 0.57545048 0.40088417
ln_exploration 0.55370676 0.28647350 0.19228822 0.19693546 0.23702112 1.000000000 0.109072879 0.33885513 0.89826000
ln_len_unique 0.38531970 0.57545048 0.38163565 0.43612011 0.50959864 0.338855129 0.098292010 1.00000000 0.54850475
ln_added_sum 0.58243780 0.40088417 0.27283404 0.27590912 0.36169269 0.898259998 0.095393103 0.54850475 1.00000000
ln_novelty ln_total ln_exploration ln_len_unique ln_added_sum
X -0.04741331 -0.08503546 -0.137918112 -0.01226832 -0.09633911
Unnamed..0 -0.04741331 -0.08503546 -0.137918112 -0.01226832 -0.09633911
phase 0.11614783 0.21237656 -0.230511006 0.19026679 -0.12989002
novelty 1.00000000 0.35093689 0.359696008 0.61941904 0.42763970
abs_perform_diff_best -0.26927972 -0.73163943 -0.113695104 -0.47621915 -0.23717405
Q7_Q7_1 0.08001802 -0.07189416 -0.003202928 0.12554739 0.04149820
Q7_Q7_2 0.18380978 0.14138398 0.032834551 0.21213738 0.07157854
Q8_Q8_1 0.15335427 0.14739393 -0.020931428 0.25660123 0.08453075
Q10 0.08843367 0.22020677 0.020792263 0.24104473 0.11804975
count 0.31277705 0.46015412 0.553706760 0.38531970 0.58243780
total 0.35093689 1.00000000 0.286473499 0.57545048 0.40088417
user.requirement 0.25602042 0.83475255 0.192288215 0.38163565 0.27283404
infovis 0.24858512 0.83755499 0.196935460 0.43612011 0.27590912
novelty_score 0.25940071 0.84153034 0.237021121 0.50959864 0.36169269
exploration 0.35969601 0.28647350 1.000000000 0.33885513 0.89826000
Group 0.13955343 0.16028506 0.109072879 0.09829201 0.09539310
len_unique 0.61941904 0.57545048 0.338855129 1.00000000 0.54850475
added_sum 0.42763970 0.40088417 0.898259998 0.54850475 1.00000000
ln_novelty 1.00000000 0.35093689 0.359696008 0.61941904 0.42763970
ln_total 0.35093689 1.00000000 0.286473499 0.57545048 0.40088417
ln_exploration 0.35969601 0.28647350 1.000000000 0.33885513 0.89826000
ln_len_unique 0.61941904 0.57545048 0.338855129 1.00000000 0.54850475
ln_added_sum 0.42763970 0.40088417 0.898259998 0.54850475 1.00000000
library(car)
Loading required package: carData
mod <- lm(ln_total~ ln_novelty + ln_len_unique, data=df)
vif(mod)
ln_novelty ln_len_unique
1.780538 1.780538
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_added_sum ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_added_sum ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-1.8830 -1.8139 -0.4864 1.3270 6.6483
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.86244 0.15778 11.804 <2e-16 ***
factor(group)0 -0.55034 0.22166 -2.483 0.0133 *
factor(group)1 -0.04857 0.21891 -0.222 0.8245
factor(group)2 0.02058 0.21763 0.095 0.9247
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.919 on 620 degrees of freedom
(12 observations deleted due to missingness)
Multiple R-squared: 0.01469, Adjusted R-squared: 0.009923
F-statistic: 3.081 on 3 and 620 DF, p-value: 0.02695
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_exploration ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_exploration ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-0.2275 -0.1862 -0.1563 0.1866 0.5328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.22749 0.01941 11.719 <2e-16 ***
factor(group)0 -0.06718 0.02710 -2.479 0.0134 *
factor(group)1 -0.04128 0.02678 -1.542 0.1236
factor(group)2 -0.03052 0.02662 -1.146 0.2521
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2362 on 632 degrees of freedom
Multiple R-squared: 0.009905, Adjusted R-squared: 0.005206
F-statistic: 2.108 on 3 and 632 DF, p-value: 0.09805
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_len_unique ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_len_unique ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-4.000 -1.004 0.126 1.144 5.135
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.7016 0.1562 23.705 < 2e-16 ***
factor(group)0 -0.8832 0.2194 -4.026 6.38e-05 ***
factor(group)1 0.1472 0.2167 0.679 0.497
factor(group)2 0.2984 0.2154 1.385 0.166
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.9 on 620 degrees of freedom
(12 observations deleted due to missingness)
Multiple R-squared: 0.05512, Adjusted R-squared: 0.05055
F-statistic: 12.06 on 3 and 620 DF, p-value: 1.115e-07
tapply(df$ln_len_unique, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 2.773 3.726 3.702 4.511 8.514
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 3.497 2.818 4.143 7.953 4
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.303 4.086 3.849 5.017 8.415 4
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 3.091 3.892 4.000 4.878 8.489 4
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_total ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_total ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-4.7373 -0.2143 0.3493 0.8471 1.7667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.1441 0.1181 43.541 < 2e-16 ***
factor(group)0 -1.0417 0.1649 -6.316 5.05e-10 ***
factor(group)1 -0.4069 0.1630 -2.497 0.012787 *
factor(group)2 -0.5990 0.1620 -3.697 0.000237 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.437 on 632 degrees of freedom
Multiple R-squared: 0.06155, Adjusted R-squared: 0.0571
F-statistic: 13.82 on 3 and 632 DF, p-value: 9.76e-09
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_novelty ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-0.52892 -0.14068 0.06865 0.15783 0.28954
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.52892 0.01773 29.837 < 2e-16 ***
factor(group)0 -0.13269 0.02475 -5.362 1.16e-07 ***
factor(group)1 -0.12367 0.02445 -5.058 5.56e-07 ***
factor(group)2 -0.05178 0.02431 -2.130 0.0336 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2157 on 632 degrees of freedom
Multiple R-squared: 0.05844, Adjusted R-squared: 0.05397
F-statistic: 13.08 on 3 and 632 DF, p-value: 2.706e-08
df$group <- relevel(df$group, ref = "3")
mod2 <- lm(ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod2)
Call:
lm(formula = ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.6625 -0.1580 -0.1158 0.1618 0.5694
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.233602 0.038414 6.081 2.10e-09 ***
factor(group)0 -0.055198 0.026422 -2.089 0.0371 *
factor(group)1 -0.036783 0.026092 -1.410 0.1591
factor(group)2 -0.022188 0.025726 -0.862 0.3888
Q7_Q7_1 -0.003198 0.007597 -0.421 0.6740
Q7_Q7_2 0.005396 0.007728 0.698 0.4853
Q8_Q8_1 -0.013705 0.007994 -1.714 0.0870 .
Q10 -0.003711 0.011739 -0.316 0.7520
count 0.025482 0.003090 8.248 9.92e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2257 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1106, Adjusted R-squared: 0.09895
F-statistic: 9.497 on 8 and 611 DF, p-value: 1.997e-12
df$group <- relevel(df$group, ref = "3")
mod3 <- lm(ln_exploration ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod3)
Call:
lm(formula = ln_exploration ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.6609 -0.1563 -0.1278 0.1708 0.5594
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.201974 0.033922 5.954 4.40e-09 ***
Q7_Q7_1 -0.003721 0.007550 -0.493 0.622
Q7_Q7_2 0.006447 0.007670 0.841 0.401
Q8_Q8_1 -0.012414 0.007974 -1.557 0.120
Q10 -0.006051 0.011524 -0.525 0.600
count 0.025721 0.003089 8.326 5.47e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.226 on 614 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1038, Adjusted R-squared: 0.09647
F-statistic: 14.22 on 5 and 614 DF, p-value: 3.509e-13
anova(mod2, mod3)
Analysis of Variance Table
Model 1: ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 +
Q10 + count
Model 2: ln_exploration ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
Res.Df RSS Df Sum of Sq F Pr(>F)
1 611 31.133
2 614 31.372 -3 -0.23919 1.5647 0.1968
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod)
Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.73108 -0.10789 0.05269 0.14730 0.30517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.412100 0.035171 11.717 < 2e-16 ***
factor(group)0 -0.113961 0.024192 -4.711 3.06e-06 ***
factor(group)1 -0.116408 0.023889 -4.873 1.40e-06 ***
factor(group)2 -0.051286 0.023555 -2.177 0.02984 *
Q7_Q7_1 -0.020611 0.006956 -2.963 0.00316 **
Q7_Q7_2 0.028904 0.007075 4.085 4.99e-05 ***
Q8_Q8_1 0.008860 0.007319 1.210 0.22656
Q10 0.007122 0.010748 0.663 0.50783
count 0.013293 0.002829 4.699 3.23e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2067 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1234, Adjusted R-squared: 0.112
F-statistic: 10.75 on 8 and 611 DF, p-value: 3.249e-14
df$group <- relevel(df$group, ref = "3")
mod1 <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod1)
Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.73108 -0.10789 0.05269 0.14730 0.30517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.412100 0.035171 11.717 < 2e-16 ***
factor(group)0 -0.113961 0.024192 -4.711 3.06e-06 ***
factor(group)1 -0.116408 0.023889 -4.873 1.40e-06 ***
factor(group)2 -0.051286 0.023555 -2.177 0.02984 *
Q7_Q7_1 -0.020611 0.006956 -2.963 0.00316 **
Q7_Q7_2 0.028904 0.007075 4.085 4.99e-05 ***
Q8_Q8_1 0.008860 0.007319 1.210 0.22656
Q10 0.007122 0.010748 0.663 0.50783
count 0.013293 0.002829 4.699 3.23e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2067 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1234, Adjusted R-squared: 0.112
F-statistic: 10.75 on 8 and 611 DF, p-value: 3.249e-14
df$group <- relevel(df$group, ref = "3")
mod4 <- lm(ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod4)
Call:
lm(formula = ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.7883 -0.0854 0.0699 0.1531 0.3014
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.343113 0.031746 10.808 < 2e-16 ***
Q7_Q7_1 -0.023135 0.007066 -3.274 0.00112 **
Q7_Q7_2 0.032111 0.007178 4.474 9.17e-06 ***
Q8_Q8_1 0.011171 0.007462 1.497 0.13490
Q10 -0.001228 0.010785 -0.114 0.90939
count 0.013646 0.002891 4.720 2.93e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2115 on 614 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.07716, Adjusted R-squared: 0.06964
F-statistic: 10.27 on 5 and 614 DF, p-value: 1.82e-09
anova(mod1, mod4)
Analysis of Variance Table
Model 1: ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count
Model 2: ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
Res.Df RSS Df Sum of Sq F Pr(>F)
1 611 26.099
2 614 27.477 -3 -1.3777 10.751 6.815e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
library(lmerTest)
fit.lmer <- lmer(ln_novelty ~ factor(group) + ( 1 | phase), data = df, REML= FALSE)
fit.lmer
Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
Formula: ln_novelty ~ factor(group) + (1 | phase)
Data: df
AIC BIC logLik deviance df.resid
-138.4479 -111.7167 75.2239 -150.4479 630
Random effects:
Groups Name Std.Dev.
phase (Intercept) 0.005242
Residual 0.214918
Number of obs: 636, groups: phase, 4
Fixed Effects:
(Intercept) factor(group)0 factor(group)1 factor(group)2
0.52892 -0.13269 -0.12367 -0.05178
tapply(df$ln_novelty, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.4842 0.5588 0.5289 0.6162 0.6894
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.5206 0.3962 0.6073 0.6858
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.1777 0.5062 0.4053 0.6182 0.6931
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.3871 0.5465 0.4771 0.6084 0.6904
tapply(df$ln_total, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.331 4.761 5.079 5.144 5.515 5.891
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 3.991 4.830 4.102 5.337 5.869
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.553 5.089 4.737 5.580 5.882
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.615 4.925 4.545 5.450 5.884
tapply(df$ln_exploration, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.0938 0.2275 0.4391 0.6931
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.0000 0.1603 0.3010 0.6931
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00000 0.00000 0.02175 0.18621 0.38244 0.69315
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00000 0.00000 0.09391 0.19697 0.35899 0.69315
tapply(df$ln_len_unique, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 2.773 3.726 3.702 4.511 8.514
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 3.497 2.818 4.143 7.953 4
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.303 4.086 3.849 5.017 8.415 4
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 3.091 3.892 4.000 4.878 8.489 4
tapply(df$ln_added_sum, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 0.000 1.792 1.862 3.091 8.511
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 0.000 1.312 2.788 7.945 4
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 1.609 1.814 3.091 8.027 4
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 0.000 1.792 1.883 3.178 8.484 4
library(vtree)
Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
vtree version 5.6.5 -- For more information, type: vignette("vtree")
vtree(df, "group")
vtree(df, c("phase", "group"),
fillcolor = c( phase = "#e7d4e8", group = "#99d8c9"),
horiz = FALSE)
df$group <- relevel(df$group, ref = "3")
mod5 <- lm(ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod5)
Call:
lm(formula = ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 +
Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-4.6309 -0.2310 0.3346 0.7764 1.9667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.82832 0.22926 21.060 < 2e-16 ***
factor(group)0 -0.98353 0.15769 -6.237 8.33e-10 ***
factor(group)1 -0.42360 0.15572 -2.720 0.006709 **
factor(group)2 -0.59841 0.15354 -3.897 0.000108 ***
Q7_Q7_1 -0.19585 0.04534 -4.319 1.83e-05 ***
Q7_Q7_2 0.19627 0.04612 4.256 2.41e-05 ***
Q8_Q8_1 -0.10504 0.04771 -2.202 0.028060 *
Q10 0.17920 0.07006 2.558 0.010776 *
count 0.12749 0.01844 6.914 1.19e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.347 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1768, Adjusted R-squared: 0.166
F-statistic: 16.4 on 8 and 611 DF, p-value: < 2.2e-16
df$group <- relevel(df$group, ref = "3")
mod6 <- lm(ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod6)
Call:
lm(formula = ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count,
data = df)
Residuals:
Min 1Q Median 3Q Max
-4.5737 -0.1258 0.3665 0.7666 1.7353
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.19765 0.20821 20.160 < 2e-16 ***
Q7_Q7_1 -0.18970 0.04634 -4.093 4.82e-05 ***
Q7_Q7_2 0.19885 0.04708 4.224 2.77e-05 ***
Q8_Q8_1 -0.07884 0.04894 -1.611 0.1077
Q10 0.17509 0.07073 2.475 0.0136 *
count 0.13321 0.01896 7.025 5.71e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.387 on 614 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1226, Adjusted R-squared: 0.1154
F-statistic: 17.16 on 5 and 614 DF, p-value: 6.62e-16
anova(mod5, mod6)
Analysis of Variance Table
Model 1: ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count
Model 2: ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
Res.Df RSS Df Sum of Sq F Pr(>F)
1 611 1109
2 614 1182 -3 -73.013 13.409 1.744e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
with(df, interaction.plot(group, phase, ln_total, ylim=c(0, max(ln_total)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot

with(df, interaction.plot(group, phase, ln_exploration, ylim=c(0, max(ln_exploration)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot

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